Public Health Unit Funding and Emergency Department Visits due to Self-Harm, Alcohol, and Drug Poisoning Before and During COVID-19 in Ontario, Canada
Bibliographic record
Abstract
Background: Self-harm, alcohol use, and drug poisoning harms (SAD) are major public health concerns in Canada. Since the early 2000s, SAD deaths, hospitalizations, and ED visits have increased over time, and it was predicted that the COVID-19 pandemic may have exacerbated these conditions in Canada. Current research suggest that increased government public health funding towards mental health and substance use programs was associated with decreased rates of SAD hospitalization and emergency department (ED) visits. However, this research consists of mainly ecological or simulated studies, with a lack of observational cohort data that examines whether public health funding is associated with individual risks of these health outcomes. Objectives: The study objectives were to: 1) estimate the association between local public health unit (PHU) funding per capita with SAD ED visits among individuals aged 15+ years living in Ontario, Canada between April 1st 2018-March 31st 2020 (before COVID-19) and April 1st 2020-March 31st 2022 (during COVID-19), and 2) to test for effect modification and determine whether the observed associations were heterogeneous across age, gender, ethnicity, rurality, and socio-economic status. Method: This was a cohort study using data from the Canadian Census Health and Environmental Cohorts (CanCHEC) which includes individuals from the 2016 Census linked to National Ambulatory Care Reporting System (NACRS) for follow up on ED visits. Individuals in this study were linked to Ontario Public Health Information Database (OPHID) using the location of the PHU in which they resided at the start of baseline in order to determine their exposure to PHU funding at the start of follow-up. Socioeconomic characteristics of this cohort were obtained from census respondents of CanCHEC 2016. Multilevel mixed effects survival models were conducted. Survey weights were applied to generalize the findings. Results: There were 34 PHUs in 2018 and 33 PHUs in 2020 for analysis. The unweighted baseline samples (rounded to the nearest 5) included 2,435,300 individuals in 2018 and 2,417,640 individuals in 2020 aged 15 and over. A small proportion, <1% of the sample, had a SAD ED visit. A $10 increase of total public health funding per capita was associated with a decrease hazards of self-harm ED visits (adjusted hazard ratio (aHR): 0.96; 95% CI: 0.92-0.99) before COVID-19 and alcohol-attributable ED visits (aHR: 0.93; 95% CI: 0.88-0.98) during COVID-19. An increase of Substance Use and Injury Prevention (SUIP) funding per capita by $2 was associated with decreased hazards of alcohol ED visits (aHR: 0.93; 95% CI: 0.85-1.01) before COVID-19 and decreased hazards of self-harm ED visits (aHR: 0.95; 95% CI: 0.92-0.98) during COVID-19. Cross-level interactions indicated that increased PHU funding per capita was associated with: decreased self-harm and drug poisoning ED visits among youth aged 15-24, decreased alcohol ED visits among males and middle adults aged 45-64, and decreased SAD ED visits among those with moderate income and residing in mixed, rural, and northern PHU regions. Conclusion: Increased PHU funding was associated with decreased self-harm and alcohol attributable ED visits. Increased PHU funding was mainly associated with groups at highest risk of SAD ED visits, such as young females and middle adult males living in northern or rural PHUs, with exception to those with low income. Although PHU funding may be an important tool to enhance mental health and substance services in Ontario, further research is needed to apply health equity to low-income groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".